Visual preferences for communicating modelling: a global analysis of COVID-19 policy and decision makers

IF 8.8 3区 医学 Q1 Medicine
Liza Hadley , Caylyn Rich , Alex Tasker , Olivier Restif , Sebastian Funk
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引用次数: 0

Abstract

Effective communication of modelling results to policy and decision makers has been a longstanding challenge in times of crises. This communication takes many forms - visualisations, reports, presentations - and requires careful consideration to ensure accurate maintenance of the key scientific messages. Science-to-policy communication is further exacerbated when presenting fundamentally uncertain forms of science such as infectious disease modelling and other types of modelled evidence, something which has been understudied. Here we assess the communication and visualisation of infectious disease modelling results to national COVID-19 policy and decision makers in 13 different countries. We present a synthesis of recommendations on what aspects of visuals, graphs, and plots policymakers found to be most helpful in their COVID-19 response work. This work serves as a first evidence base for developing guidelines on the communication and translation of infectious disease modelling into policy.
沟通建模的视觉偏好:对COVID-19政策和决策者的全球分析
在危机时期,将建模结果有效地传达给政策和决策者一直是一项长期挑战。这种传播有多种形式——可视化、报告、演示——并且需要仔细考虑以确保准确地维护关键的科学信息。当提出根本不确定的科学形式,如传染病建模和其他类型的模拟证据时,科学与政策的交流进一步加剧,而这些科学形式一直没有得到充分的研究。在这里,我们评估了传染病建模结果与13个不同国家的国家COVID-19政策和决策者的沟通和可视化。我们就决策者认为视觉、图表和图表的哪些方面对其COVID-19应对工作最有帮助提出了综合建议。这项工作是制定关于传染病模型的传播和转化为政策的准则的第一个证据基础。
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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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